{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>theme</th>\n",
       "      <th>URL</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>汽车</td>\n",
       "      <td>新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价</td>\n",
       "      <td>http://auto.data.people.com.cn/model_15782/</td>\n",
       "      <td>经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>汽车</td>\n",
       "      <td>９１８　Ｓｐｙｄｅｒ概念车</td>\n",
       "      <td>http://auto.data.people.com.cn/prdview_165423....</td>\n",
       "      <td>呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>汽车</td>\n",
       "      <td>日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_5249...</td>\n",
       "      <td>ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>汽车</td>\n",
       "      <td>清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_6144...</td>\n",
       "      <td>清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>汽车</td>\n",
       "      <td>大众敞篷家族新成员　高尔夫敞篷版实拍</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_5686...</td>\n",
       "      <td>在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                                 theme  \\\n",
       "0       汽车  新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价   \n",
       "1       汽车                         ９１８　Ｓｐｙｄｅｒ概念车   \n",
       "2       汽车              日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎   \n",
       "3       汽车                清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万   \n",
       "4       汽车                    大众敞篷家族新成员　高尔夫敞篷版实拍   \n",
       "\n",
       "                                                 URL  \\\n",
       "0        http://auto.data.people.com.cn/model_15782/   \n",
       "1  http://auto.data.people.com.cn/prdview_165423....   \n",
       "2  http://auto.data.people.com.cn/news/story_5249...   \n",
       "3  http://auto.data.people.com.cn/news/story_6144...   \n",
       "4  http://auto.data.people.com.cn/news/story_5686...   \n",
       "\n",
       "                                             content  \n",
       "0  经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...  \n",
       "1       呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ  \n",
       "2  ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...  \n",
       "3  清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...  \n",
       "4  在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news = pd.read_table('./data/val.txt',names = ['category','theme','URL','content'])\n",
    "df_news = df_news.dropna()\n",
    "df_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'阿里巴巴集团昨日宣布，将在集团管理层面设立首席数据官岗位（Ｃｈｉｅｆ\\u3000Ｄａｔａ\\u3000Ｏｆｆｉｃｅｒ），阿里巴巴Ｂ２Ｂ公司ＣＥＯ陆兆禧将会出任上述职务，向集团ＣＥＯ马云直接汇报。＞菹ぃ和６月初的首席风险官职务任命相同，首席数据官亦为阿里巴巴集团在完成与雅虎股权谈判，推进“ｏｎｅ\\u3000ｃｏｍｐａｎｙ”目标后，在集团决策层面新增的管理岗位。０⒗锛团昨日表示，“变成一家真正意义上的数据公司”已是战略共识。记者刘夏'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content = df_news.content.values.tolist()\n",
    "content[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 1.738 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    }
   ],
   "source": [
    "content_S = []\n",
    "for line in content:\n",
    "    current_segment = jieba.lcut(line)\n",
    "    if len(current_segment) > 1 and current_segment != '\\r\\t':\n",
    "        content_S.append(current_segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['阿里巴巴',\n",
       " '集团',\n",
       " '昨日',\n",
       " '宣布',\n",
       " '，',\n",
       " '将',\n",
       " '在',\n",
       " '集团',\n",
       " '管理',\n",
       " '层面',\n",
       " '设立',\n",
       " '首席',\n",
       " '数据',\n",
       " '官',\n",
       " '岗位',\n",
       " '（',\n",
       " 'Ｃ',\n",
       " 'ｈ',\n",
       " 'ｉ',\n",
       " 'ｅ',\n",
       " 'ｆ',\n",
       " '\\u3000',\n",
       " 'Ｄ',\n",
       " 'ａ',\n",
       " 'ｔ',\n",
       " 'ａ',\n",
       " '\\u3000',\n",
       " 'Ｏ',\n",
       " 'ｆ',\n",
       " 'ｆ',\n",
       " 'ｉ',\n",
       " 'ｃ',\n",
       " 'ｅ',\n",
       " 'ｒ',\n",
       " '）',\n",
       " '，',\n",
       " '阿里巴巴',\n",
       " 'Ｂ',\n",
       " '２',\n",
       " 'Ｂ',\n",
       " '公司',\n",
       " 'Ｃ',\n",
       " 'Ｅ',\n",
       " 'Ｏ',\n",
       " '陆兆禧',\n",
       " '将',\n",
       " '会',\n",
       " '出任',\n",
       " '上述',\n",
       " '职务',\n",
       " '，',\n",
       " '向',\n",
       " '集团',\n",
       " 'Ｃ',\n",
       " 'Ｅ',\n",
       " 'Ｏ',\n",
       " '马云',\n",
       " '直接',\n",
       " '汇报',\n",
       " '。',\n",
       " '＞',\n",
       " '菹',\n",
       " 'ぃ',\n",
       " '和',\n",
       " '６',\n",
       " '月初',\n",
       " '的',\n",
       " '首席',\n",
       " '风险',\n",
       " '官',\n",
       " '职务',\n",
       " '任命',\n",
       " '相同',\n",
       " '，',\n",
       " '首席',\n",
       " '数据',\n",
       " '官亦为',\n",
       " '阿里巴巴',\n",
       " '集团',\n",
       " '在',\n",
       " '完成',\n",
       " '与',\n",
       " '雅虎',\n",
       " '股权',\n",
       " '谈判',\n",
       " '，',\n",
       " '推进',\n",
       " '“',\n",
       " 'ｏ',\n",
       " 'ｎ',\n",
       " 'ｅ',\n",
       " '\\u3000',\n",
       " 'ｃ',\n",
       " 'ｏ',\n",
       " 'ｍ',\n",
       " 'ｐ',\n",
       " 'ａ',\n",
       " 'ｎ',\n",
       " 'ｙ',\n",
       " '”',\n",
       " '目标',\n",
       " '后',\n",
       " '，',\n",
       " '在',\n",
       " '集团',\n",
       " '决策',\n",
       " '层面',\n",
       " '新增',\n",
       " '的',\n",
       " '管理',\n",
       " '岗位',\n",
       " '。',\n",
       " '０',\n",
       " '⒗',\n",
       " '锛',\n",
       " '团',\n",
       " '昨日',\n",
       " '表示',\n",
       " '，',\n",
       " '“',\n",
       " '变成',\n",
       " '一家',\n",
       " '真正',\n",
       " '意义',\n",
       " '上',\n",
       " '的',\n",
       " '数据',\n",
       " '公司',\n",
       " '”',\n",
       " '已',\n",
       " '是',\n",
       " '战略',\n",
       " '共识',\n",
       " '。',\n",
       " '记者',\n",
       " '刘夏']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_S[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>[经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...</td>\n",
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      ],
      "text/plain": [
       "                                           content_S\n",
       "0  [经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...\n",
       "1  [呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...\n",
       "3  [清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...\n",
       "4  [在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_content = pd.DataFrame({'content_S':content_S})\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  stopword\n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "stopwords = pd.read_table('stopwords.txt',names = ['stopword'],quoting = 3)\n",
    "stopwords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def drop_stopwords(contents,stopwords):\n",
    "    contents_clean = []\n",
    "    all_words = []\n",
    "    for line in contents:\n",
    "        line_clean = []\n",
    "        for word in line:\n",
    "            if word in stopwords:\n",
    "                continue\n",
    "            line_clean.append(word)\n",
    "            all_words.append(word)\n",
    "        contents_clean.append(line_clean)\n",
    "    return contents_clean,all_words\n",
    "contents = df_content.content_S.values.tolist()\n",
    "stopwords = stopwords.stopword.values.tolist()\n",
    "contents_clean,all_words = drop_stopwords(contents,stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_content = pd.DataFrame({'contents_clean':contents_clean})\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>经销商</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>电话</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>试驾</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>订车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ｕ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  all_words\n",
       "0       经销商\n",
       "1        电话\n",
       "2        试驾\n",
       "3        订车\n",
       "4         Ｕ"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_words = pd.DataFrame({'all_words':all_words})\n",
    "df_all_words.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3.5.0.1\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4077</th>\n",
       "      <td>中</td>\n",
       "      <td>5199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4209</th>\n",
       "      <td>中国</td>\n",
       "      <td>3115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88255</th>\n",
       "      <td>说</td>\n",
       "      <td>3055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104747</th>\n",
       "      <td>Ｓ</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1373</th>\n",
       "      <td>万</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       all_words  count\n",
       "4077           中   5199\n",
       "4209          中国   3115\n",
       "88255          说   3055\n",
       "104747         Ｓ   2646\n",
       "1373           万   2390"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_count = df_all_words.groupby(by=['all_words'])['all_words'].agg({'count':np.size})\n",
    "words_count = words_count.reset_index().sort_values(by=['count'],ascending = False)\n",
    "words_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (20.0,10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15e57e10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HllqnHeyF96/t8DEFIgdq2b1IcTPO0WFatIglBmNifacFo7UjNTGE9TVo/GPH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mdueTvkmG/f/C3o48edf2RY62otoSxP7Y1O2SdcTYHvuS2uc76zuCIsMM0wHUWoAsPTLjha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\nn8rakk9lbG8R4zIjZLBsq2uulHGUv6gGtaro/Ta9l5pt/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9ys+RlAHyOzuj9E4rbF3dwn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\ni/h/LWLdqs/qP+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b77530>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(font_path='./data/simhei.ttf',background_color='white',max_font_size=100)\n",
    "word_frequency = {x[0]:x[1] for x in words_count.head(200).values}\n",
    "wordcloud = wordcloud.fit_words(word_frequency)\n",
    "plt.imshow(wordcloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "明晚７点４５分，申花将在主场迎战北京国安，而德罗巴也将首次在中国球迷面前亮相。１３日下午７点，国安在虹口足球场召开发布会，帕切科表示：“就像一直以来一样，我们到每个客场都是为了取胜。联赛每场都很困难，没有容易的比赛，所以每场比赛我们都会尽全力去打好。我们会在胜利的前提下把比赛踢得很漂亮，然后把３分带走。上海有很好的阵容储备，但是他们在换帅的过程中不够稳定，这可以理解，当然一支球队频繁换帅，不是非常好的事情。但是他们有非常好的队员，所以明天的比赛会很精彩，双方都会尽全力把比赛打好。”Ｐ录用说耐庠格隆表示：“明天的比赛会非常精彩，上海的队员非常出色，我在录像中了解过了，我们的球队也会表现得非常出色，把３分带走。”＜钦撸嚎ㄅ特的状态怎么样，他明天会出场吗？Ｅ燎锌疲何业拿扛龆釉倍甲急负妹魈斓恼蕉罚卡努特来队的时间还短，还需要一点时间与球队磨合。所以明天卡努特不会打主力。＜钦撸焊据京沪两地的传统来看，北京连续两个客场在虹口输球，明天的胜算有多大？Ｅ燎锌疲何蚁嘈琶魈斓谋热要比去年会更好，因为去年来上海的时候，我们已经没有悬念地取得了联赛的第二名，这或多或少地会影响到队员的心态，虽然我不允许出现这种情况，但是我也无法完全阻止，但是明天我相信我的队员会带着极大的野心把这３分带走。＜钦撸呵胛式塘分不知道职业联赛以来，我们从来没有在虹口赢过球？Ｅ燎锌疲何也恢道，但是我不是一个迷信的人，我不去管之前战绩如何，每场比赛都是新的历史。上海还１比９输给国安，但是这些并不重要，当下才是最重要的。我们不要管历史上怎么样，最重要的是我对我们的队员都很有信心，明天我们会把比赛踢好，把比赛拿下来，这才是我们最重要的目标。＜钦撸耗芊衿兰垡幌驴ㄅ特和即将到来的德罗巴？Ｅ燎锌疲合衷诙杂诳ㄅ特和德罗巴作出评价还不够公正，因为他们还没有开始在这个联赛踢比赛，但是他们都是毫无疑问的高水平球员，像卡努特无论在法国在热刺在塞维利亚都踢得很好，一个队员在一个俱乐部能踢很多年，而且占据主力位置，这也是对他能力和人品的双重证明，这些我们可以从历史上看出来。德罗巴就更不用说了，他是欧洲最顶级联赛最顶级的队员，他的能力毋庸置疑，但是要看他在中超联赛最终的表现如何。我相信他们会对中超联赛作出很大的推动。无论是卡努特还是德罗巴还是格隆，都能够胜任欧洲的最顶级联赛。但是他们来到这里都是带着热情和对于足球的信任来这里，我相信他们在这里有很多的机会和很好的前途。\n"
     ]
    }
   ],
   "source": [
    "import jieba.analyse\n",
    "index = 2300\n",
    "print(df_news['content'][index])\n",
    "content_S_str = ''.join(content_S[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "德罗巴 比赛 卡努特 队员 联赛 明天 但是 我们 每场 球队\n"
     ]
    }
   ],
   "source": [
    "print(' '.join(jieba.analyse.extract_tags(content_S_str,topK=10,withWeight=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3.5.0.1\\lib\\site-packages\\gensim\\utils.py:860: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "from gensim import corpora,models,similarities\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dictionary = corpora.Dictionary(contents_clean)\n",
    "corpus = [dictionary.doc2bow(sentence) for sentence in contents_clean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lda = gensim.models.ldamodel.LdaModel(corpus=corpus,id2word=dictionary,num_topics=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007*\"ａ\" + 0.006*\"中\" + 0.005*\"ｅ\" + 0.005*\"天籁\" + 0.005*\"ｒ\"\n"
     ]
    }
   ],
   "source": [
    "print(lda.print_topic(5,topn = 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006*\"中\" + 0.005*\"饰演\" + 0.003*\"两人\" + 0.003*\"说\" + 0.002*\"志愿\"\n",
      "0.004*\"食物\" + 0.004*\"中\" + 0.004*\"吃\" + 0.003*\"学生\" + 0.003*\"营养\"\n",
      "0.006*\"中\" + 0.004*\"比赛\" + 0.004*\"中国\" + 0.003*\"万\" + 0.003*\"公司\"\n",
      "0.006*\"官兵\" + 0.005*\"发展\" + 0.004*\"教育\" + 0.004*\"中\" + 0.003*\"中国\"\n",
      "0.022*\"男人\" + 0.021*\"女人\" + 0.004*\"万\" + 0.003*\"中\" + 0.003*\"老公\"\n",
      "0.007*\"ａ\" + 0.006*\"中\" + 0.005*\"ｅ\" + 0.005*\"天籁\" + 0.005*\"ｒ\"\n",
      "0.006*\"考生\" + 0.003*\"中\" + 0.003*\"万\" + 0.002*\"号\" + 0.002*\"歌迷\"\n",
      "0.011*\"中国\" + 0.008*\"中\" + 0.006*\"球队\" + 0.006*\"选手\" + 0.003*\"比赛\"\n",
      "0.007*\"万\" + 0.006*\"号\" + 0.004*\"中\" + 0.003*\"转\" + 0.002*\"米\"\n",
      "0.008*\"男人\" + 0.006*\"说\" + 0.005*\"中\" + 0.004*\"孩子\" + 0.004*\"做\"\n",
      "0.005*\"中\" + 0.003*\"说\" + 0.003*\"工作\" + 0.002*\"时间\" + 0.002*\"女儿\"\n",
      "0.005*\"创作\" + 0.005*\"中国\" + 0.005*\"学校\" + 0.004*\"招生\" + 0.003*\"Ｌ\"\n",
      "0.006*\"中\" + 0.006*\"节目\" + 0.004*\"说\" + 0.004*\"ａ\" + 0.003*\"Ｔ\"\n",
      "0.004*\"中\" + 0.003*\"幽默\" + 0.003*\"发展\" + 0.002*\"西班牙\" + 0.002*\"裙\"\n",
      "0.006*\"张绍\" + 0.005*\"中\" + 0.005*\"观众\" + 0.004*\"比赛\" + 0.003*\"爆料\"\n",
      "0.026*\"ｅ\" + 0.020*\"ａ\" + 0.020*\"ｏ\" + 0.018*\"ｉ\" + 0.018*\"ｎ\"\n",
      "0.006*\"中\" + 0.005*\"说\" + 0.004*\"学生\" + 0.003*\"比赛\" + 0.002*\"Ｎ\"\n",
      "0.011*\"ａ\" + 0.006*\"ｒ\" + 0.005*\"ｅ\" + 0.005*\"ｉ\" + 0.005*\"ｏ\"\n",
      "0.005*\"说\" + 0.005*\"中\" + 0.003*\"Ｍ\" + 0.003*\"节目\" + 0.003*\"表演\"\n",
      "0.010*\"电影\" + 0.008*\"中\" + 0.007*\"导演\" + 0.004*\"影片\" + 0.004*\"说\"\n"
     ]
    }
   ],
   "source": [
    "for topic in lda.print_topics(num_topics=20,num_words=5):\n",
    "    print(topic[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>[天气, 炎热, 补水, 变得, 美国, 跑步, 世界, 杂志, 报道, 喝水, 身体, 补...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>[不想, 说, 话, 刺激, 说, 做, 只能, 走, 离开, 伤心地, 想起, 一句, 话...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>[岁, 刘晓庆, 最新, 嫩照, Ｏ, 衷, 诘, 牧跸, 庆, 看不出, 岁, 秒杀, 刘...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>[导语, 做, 爸爸, 一种, 幸福, 无论是, 领养, 亲生, 更何况, 影视剧, 中, ...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>[全球, 最美, 女人, 合成图, 国, 整形外科, 教授, 李承哲, 国际, 学术, 杂志...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         contents_clean label\n",
       "4995  [天气, 炎热, 补水, 变得, 美国, 跑步, 世界, 杂志, 报道, 喝水, 身体, 补...    时尚\n",
       "4996  [不想, 说, 话, 刺激, 说, 做, 只能, 走, 离开, 伤心地, 想起, 一句, 话...    时尚\n",
       "4997  [岁, 刘晓庆, 最新, 嫩照, Ｏ, 衷, 诘, 牧跸, 庆, 看不出, 岁, 秒杀, 刘...    时尚\n",
       "4998  [导语, 做, 爸爸, 一种, 幸福, 无论是, 领养, 亲生, 更何况, 影视剧, 中, ...    时尚\n",
       "4999  [全球, 最美, 女人, 合成图, 国, 整形外科, 教授, 李承哲, 国际, 学术, 杂志...    时尚"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.DataFrame({'contents_clean':contents_clean,'label':df_news['category']})\n",
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['汽车', '财经', '科技', '健康', '体育', '教育', '文化', '军事', '娱乐', '时尚'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean  label\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...      1\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...      1\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...      1\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...      1\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...      1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_mapping = {\"汽车\": 1, \"财经\": 2, \"科技\": 3, \"健康\": 4, \"体育\":5, \"教育\": 6,\"文化\": 7,\"军事\": 8,\"娱乐\": 9,\"时尚\": 0}\n",
    "df_train['label'] = df_train['label'].map(label_mapping)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(df_train['contents_clean'].values,df_train['label'].values,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'上海'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bird', 'cat', 'dog', 'fish']\n",
      "[[0 1 1 1]\n",
      " [0 2 1 0]\n",
      " [1 0 0 1]\n",
      " [1 0 0 0]]\n",
      "[2 3 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "texts=[\"dog cat fish\",\"dog cat cat\",\"fish bird\", 'bird']\n",
    "cv = CountVectorizer()\n",
    "cv_fit=cv.fit_transform(texts)\n",
    "\n",
    "print(cv.get_feature_names())\n",
    "print(cv_fit.toarray())\n",
    "\n",
    "\n",
    "print(cv_fit.toarray().sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'中 报 发布 之际 多家 券商 上半年 银行 整体 利润 增速 下滑 概率 事件 国泰君安 报告 预计 各家 银行 适度 提拨 备 费用 平滑 利润 上半年 家 上市 银行 净利润 增 上半年 家 上市 银行 净利润 平均 增幅 中信 建投 证券 银行业 分析师 杨荣 媒体 中国 银行业 面对 三个 转折点 盈利 转折点 资产 质量 转折点 业务 经营 模式 转折点 率 市场化 存贷款 利差 缩窄 挤压 银行 利润 银行 业内人士 坦言 上半年 银行 收益 影响 实体 经济 下滑 带来 企业 信贷 需求 下降 Ｒ 患倚 ⌒ 凸煞 葜 埔 行 分行 信贷 负责人 长期 合作 国有企业 减少 贷款额度 经营 计划 小企业 贷款 到期 无力 续约 预计 分行 上半年 新增 信贷 客户 去年同期 减少 有余 Ｊ 谐 构 预计 资产 质量 压力 迫使 银行 提高 拨备 挤压 银行 盈利 空间 同花顺 统计数据 显示 第一 季度末 家 上市 银行 平均 不良率 申银万国 报告 指出 预计 银行 不良率 反弹 中见 顶 顶点'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = []\n",
    "for line_index in range(len(x_train)):\n",
    "    try:\n",
    "        #x_train[line_index][word_index] = str(x_train[line_index][word_index])\n",
    "        words.append(' '.join(x_train[line_index]))\n",
    "    except:\n",
    "        print (line_index,word_index)\n",
    "words[1]        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=False, max_df=1.0, max_features=4000, min_df=1,\n",
       "        ngram_range=(1, 3), preprocessor=None, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec = CountVectorizer(analyzer='word',max_features=4000,lowercase=False,ngram_range=(1,3))\n",
    "vec.fit(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "classifier = MultinomialNB()\n",
    "classifier.fit(vec.transform(words),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'国家 公务员 考试 申论 应用文 类 试题 实质 一道 集 概括 分析 提出 解决问题 一体 综合性 试题 说 一道 客观 凝练 申发 论述 文章 题目 分析 历年 国考 申论 真题 公文 类 试题 类型 多样 包括 公文 类 事务性 文书 类 题材 从题 干 作答 材料 内容 整合 分析 无需 太 创造性 发挥 纵观 历年 申论 真题 作答 应用文 类 试题 文种 格式 作出 特别 重在 内容 考查 行文 格式 考生 平常心 面对 应用文 类 试题 准确 把握 作答 领会 内在 含义 把握 题材 主旨 材料 结构 轻松 应对 应用文 类 试题 Ｒ 弧 ⒆ 钒 盐 展文 写作 原则 Ｔ 材料 中来 应用文 类 试题 材料 总体 把握 客观 考生 材料 中来 材料 中 把握 材料 准确 理解 题材 主旨 Ｔ 政府 角度 作答 应用文 类 试题 更应 注重 政府 角度 观点 政府 角度 出发 原则 表述 观点 提出 解决 之策 考生 作答 站 政府 人员 角度 看待 提出 解决问题 Ｔ 文体 结构 形式 考查 重点 文体 结构 大部分 评分 关键点 解答 方法 薄 ⒆ ス 丶 词 明 方向 作答 题目 题干 作答 作答 方向 作答 角度 关键 向导 考生 仔细阅读 题干 作答 抓住 关键词 作答 方向 相关 要点 整理 作答 思路 年国考 地市级 真 题为 例 潦惺姓 府 宣传 推进 近海 水域 污染 整治 工作 请 给定 资料 市政府 工作人员 身份 草拟 一份 宣传 纲要 Ｒ 求 保对 宣传 内容 要点 提纲挈领 陈述 玻 体现 政府 精神 全市 各界 关心 支持 污染 整治 工作 通俗易懂 超过 字 肮 丶 词 近海 水域 污染 整治 工作 市政府 工作人员 身份 宣传 纲要 提纲挈领 陈述 体现 政府 精神 全市 各界 关心 支持 污染 整治 工作 通俗易懂 提示 归结 作答 要点 包括 污染 情况 原因 解决 对策 作答 思路 情况 原因 对策 意义 逻辑 顺序 安排 文章 结构 病 ⒋ 缶殖 龇 ⅲ 明 结构 解答 应用文 类 试题 考生 材料 整体 出发 大局 出发 高屋建瓴 把握 材料 主题 思想 事件 起因 解决 对策 阅读文章 构建 文章 结构 直至 快速 解答 场 ⒗ 硭 乘悸 罚明 逻辑 应用文 类 试题 严密 逻辑思维 情况 原因 对策 意义 考生 作答 先 弄清楚 解答 思路 统筹安排 脉络 清晰 逻辑 表达 内容 表述 础 把握 明 详略 考生 仔细阅读 分析 揣摩 应用文 类 试题 内容 答题 时要 详略 得当 主次 分明 安排 内容 增加 文章 层次感 阅卷 老师 阅卷 时能 明白 清晰 一目了然 玻埃 保蹦旯 考 考试 申论 试卷 分为 省级 地市级 两套 试卷 能力 大有 省级 申论 试题 考生 宏观 角度看 注重 深度 广度 考生 深谋远虑 地市级 试题 考生 微观 视角 观察 侧重 考查 解决 能力 考生 贯彻执行 作答 区别对待'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_words = []\n",
    "for line_index in range(len(x_test)):\n",
    "    try:\n",
    "        #x_train[line_index][word_index] = str(x_train[line_index][word_index])\n",
    "        test_words.append(' '.join(x_test[line_index]))\n",
    "    except:\n",
    "         print (line_index,word_index)\n",
    "test_words[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80479999999999996"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(vec.transform(test_words),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=False, max_df=1.0, max_features=4000, min_df=1,\n",
       "        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(analyzer='word',max_features=4000,lowercase=False)\n",
    "vectorizer.fit(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "classifier = MultinomialNB()\n",
    "classifier.fit(vectorizer.transform(words),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81520000000000004"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(vectorizer.transform(test_words),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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